Buchumschlag

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management.

Gespeichert in:
Format: Online
Sprache:Englisch
Veröffentlicht: MDPI - Multidisciplinary Digital Publishing Institute 2022
Schlagworte:
groundwater
artificial intelligence
hydrologic model
groundwater level prediction
machine learning
principal component analysis
spatiotemporal variation
uncertainty analysis
hydroinformatics
support vector machine
big data
artificial neural network
nitrogen compound
nitrogen prediction
prediction models
neural network
non-linear modeling
PACF
WANN
SVM-LF
SVM-RF
Govindpur
streamflow forecasting
Bayesian model averaging
multivariate adaptive regression spline
M5 model tree
Kernel extreme learning machines
South Korea
uncertainty
sustainability
prediction intervals
ungauged basin
streamflow simulation
satellite precipitation
atmospheric reanalysis
ensemble modeling
additive regression
bagging
dagging
random subspace
rotation forest
flood routing
Muskingum method
extension principle
calibration
fuzzy sets and systems
particle swarm optimization
EEFlux
irrigation performance
CWP
water conservation
NDVI
water resources
Daymet V3
Google Earth Engine
improved extreme learning machine (IELM)
sensitivity analysis
shortwave radiation flux density
sustainable development
n/a
thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general
Online-Zugang:ONIX_20220111_9783036517209_410
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ONIX_20220111_9783036517209_410

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